Dennis Bader
Data Scientist
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolDennis Bader is a data scientist with five years of experience applying Python, machine learning and energy-system modeling to translate physical problems into scalable IT solutions. An ETH Zurich MSc in Mechanical Engineering, he has built georeferenced optimal power flow and heat-demand simulations used to analyze decarbonization and infrastructure planning for cities and countries. At Unit8 he contributed to the open-source Darts forecasting library by implementing rho-risk for robust probabilistic evaluation and enabling multi-series forecasts. He excels at handling large, geospatial datasets and improving simulation speed and accuracy, pairing thermodynamics-based physical models with probabilistic methods such as Monte Carlo. Colleagues know him for turning detailed academic models into production-ready tools that directly inform policy and operational decisions.
5 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Mechanical Engineering, Master of Science - MS, Mechanical Engineering at ETH Zürich
German, English, Spanish, French
Github Skills (8)
pandas10
machine-learning10
time-series10
python10
numpy10
data-analysis10
scikit-learn9
scikit9
Programming languages (2)
Github contributions (5)
unit8co/darts
Sep 2021 - Jan 2023
A python library for user-friendly forecasting and anomaly detection on time series.
Role in this project:
Data Scientist / ML Engineer
Data Scientist / ML EngineerContributions:791 reviews, 210 commits, 593 PRs in 1 year 4 months
Contributions summary:Dennis primarily focused on implementing and integrating a quantile risk metric, or rho-risk, to measure and evaluate the accuracy of predicted value distributions. They implemented the rho-risk functionality for both univariate and multivariate time series, as well as handled the aggregation of the metrics across multiple series. Additionally, they improved the robustness of the library by fixing potential issues related to missing values and adding the support to the library by enabling the model to generate forecasts for multiple time-series, thus, allowing for a more accurate model assessment.
forecastingpython-libraryanomalypythontime-series-analysis
dennisbader/chess_game
Jun 2021 - Sep 2021
My python chess game from 2018 including GUI
Contributions:28 PRs, 35 pushes, 30 branches in 2 months
pythonguipython-chess
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